def test_walsh():
"""
Specific tests for Walsh IDC.
"""
# Define DIAs
DIAs = [3, 4, 5, 6]
badDIAs = [1, 2, 2.5, 3.5, 4.5, 5.5, 6.5, 7, 8]
# Define theoretical remaining active insulin after 1h for all valid DIAs
remainingInsulin = [0.67282, 0.79694, 0.866427, 0.916375]
# Test bad DIAs
for dia in badDIAs:
with pytest.raises(ValueError):
idc.WalshIDC(dia)
# Test fractions of active insulin remaining
for dia, ri in dict(zip(DIAs, remainingInsulin)).items():
walsh = idc.WalshIDC(dia)
# Test Walsh IDC
isValid(walsh)
# Test after one hour
assert isEqual(walsh.f(-1), ri)
def test_compute_iob_single_step_net():
"""
Test IOB computing for a net insulin profile with a single step.
"""
# Define a DIA
DIA = 3.0
# Get an IDC
walsh = idc.WalshIDC(DIA)
# Create net insulin profile
netInsulin = net.Net()
netInsulin.t = np.array([-DIA, 0])
netInsulin.y = np.array([1, 1])
# Define integral over single step
walshIntegrals = np.array([1.45532, 0])
expectedIOB = np.sum(netInsulin.y * walshIntegrals)
IOB = calculator.computeIOB(netInsulin, walsh)
assert isEqual(IOB, expectedIOB)
# Redefine net insulin profile that corresponds to only scheduled basals
# (there should be no insulin on board)
netInsulin.t = np.array([-DIA, 0])
netInsulin.y = [0, 0]
expectedIOB = 0
IOB = calculator.computeIOB(netInsulin, walsh)
assert isEqual(IOB, expectedIOB)
def test_compute_iob_multiple_steps_net():
"""
Test IOB computing for a net insulin profile with multiple steps.
"""
# Define a DIA
DIA = 3.0
# Get an IDC
walsh = idc.WalshIDC(DIA)
# Create net insulin profile
netInsulin = net.Net()
netInsulin.t = np.array([-DIA, -2, -1, 0])
netInsulin.y = np.array([2, -0.5, 3, 1])
# Define part integrals (one for each step)
walshIntegrals = np.array([0.129461, 0.444841, 0.881021, 0])
expectedIOB = np.sum(netInsulin.y * walshIntegrals)
IOB = calculator.computeIOB(netInsulin, walsh)
assert isEqual(IOB, expectedIOB)
def plotInsulinActivity():
"""
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
PLOTINSULINACTIVITY
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
"""
# Get current time
now = datetime.datetime.now()
# Read DIA
DIA = reporter.getPumpReport().get(["Settings", "DIA"])
# Define timestep (h)
dt = 5 / 60.
# Compute number of steps
n = int(DIA / dt)
# Generate time axis for all IOBs
t = np.linspace(DIA * 3600, 0, 500)
T = np.linspace(dt, DIA, n)
# Convert time axis to hours
t /= 3600.0
# FIXME
# Build profiles manually
# Build net insulin profile
walsh = idc.WalshIDC(DIA)
iob = FutureIOB()
bg = FutureBG()
net = Net()
# Initialize plot
mpl.rc("font", size=11, family="Ubuntu")
plt.figure(0, figsize=(10, 8))
plt.subplot(111)
# Define plot title
plt.title("Insulin Decay Over Time (DIA = " + str(DIA) + ")",
weight="semibold")
# Define plot axis
plt.xlabel("Time (h)", weight="semibold")
plt.ylabel("Insulin Activity (-)", weight="semibold")
# Add Walsh IDC to plot
plt.plot(-t, walsh.f(t), ls="-", lw=1.5, c="red", label="Walsh IDC")
# Add insulin net profile to plot
plt.step(-net.T,
np.append(0, net.y[:-1]),
ls="-",
lw=1.5,
c="black",
label="Net Profile")
# Add future IOBs to plot
plt.plot(T, iob.y, ls="-", lw=1.5, c="purple", label="Future IOB")
# Add eventual BGs to plot
plt.plot(T, bg.y, ls="-", lw=1.5, c="blue", label="Eventual BG")
# Define plot legend
legend = plt.legend(title="Legend",
loc=0,
borderaxespad=1.5,
numpoints=1,
markerscale=2)
# Style legend
plt.setp(legend.get_title(), fontweight="semibold")
# Tighten up
plt.tight_layout()
# Show plot
plt.show()
def modelInsulinActivity(t, args, PIA, DIA, MID):
"""
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
MODELINSULINACTIVITY
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
"""
# Define IDC of Animas
x0 = 1.0104
x1 = -0.02203
x2 = -0.2479
x3 = 0.07493
x4 = -0.006623
tAnimas = t[0:(4.5 * len(t) / DIA)]
IDCAnimas = (x0 + x1 * tAnimas + x2 * tAnimas**2 + x3 * tAnimas**3 +
x4 * tAnimas**4)
# Extract optimized parameters
a = args[0]
b = args[1]
c = args[2]
# Initialize plot
mpl.rc("font", size=11, family="Ubuntu")
fig = plt.figure(0, figsize=(10, 8))
sub = plt.subplot(111)
# Define plot title
plt.title("Insulin activity and decay over time for " + "PIA = " +
str(PIA) + ", DIA = " + str(DIA) + ", and MID = " + str(MID),
weight="semibold")
# Define plot axis
plt.xlabel("Time (h)", weight="semibold")
plt.ylabel("Insulin Activity (-)", weight="semibold")
# Add IAC and its integral to plot
plt.plot(t,
IAC(t=t, args=args),
ls="-",
lw=1.5,
c="grey",
label="IAC: " + r"$f(t) = a \cdot x^b \cdot e^{-c \cdot t}$, " +
"with $[a, b, c]$ = [" + str(round(a, 1)) + ", " +
str(round(b, 1)) + ", " + str(round(c, 1)) + "]")
plt.plot(t,
IDC(t=t, args=args),
ls="-",
lw=1.5,
c="blue",
label="IDC: " + r"$F(t) = \int$" + " " + r"$f(t) \cdot dt$")
plt.plot(tAnimas,
IDCAnimas,
ls="-",
lw=1.5,
c="purple",
label="Animas IDC")
walshIDC = idc.WalshIDC(3)
plt.plot(t, walshIDC.f(t=t), ls="-", lw=1.5, c="red", label="Walsh IDC")
plt.plot(t, walshIDC.F(t=t), ls="-", lw=1.5, c="orange", label="Walsh IDC")
# Define plot legend
legend = plt.legend(title="Insulin activity and decay curves",
loc=1,
borderaxespad=1.5,
numpoints=1,
markerscale=2)
plt.setp(legend.get_title(), fontweight="semibold")
# Tighten up
plt.tight_layout()
# Show plot
plt.show()